用脑力流失算法解八皇后问题，效果优于传统方法。
Application of the Brain Drain Optimization Algorithm to the N-Queens Problem
- 借鉴人才外流机制设计新型群智能优化算法
- 在多个指标上优于PSO、GA等经典算法
- 适合解决组合优化类问题，可拓展至AI其他领域
本文将受智力精英外流启发的脑力流失优化算法（BRADO）应用于经典的八皇后组合优化问题。通过设计成本函数引导搜索，并采用基于TOPSIS的多准则决策过程调整配置。实验表明，BRADO在解的质量上持续优于多种主流元启发式算法，包括粒子群优化（PSO）、遗传算法（GA）、帝国竞争算法（ICA）、迭代局部搜索（ILS）和基础局部搜索（LS），在威胁数量和目标函数值方面表现更优。该研究验证了BRADO作为通用组合优化求解器的潜力，为人工智能其他领域的应用提供了新路径。
原文摘要 · Abstract (English)
This paper introduces the application of the Brain Drain Optimization algorithm -- a swarm-based metaheuristic inspired by the emigration of intellectual elites -- to the N-Queens problem. The N-Queens problem, a classic combinatorial optimization problem, serves as a challenge for applying the BRADO. A designed cost function guides the search, and the configurations are tuned using a TOPSIS-based multicriteria decision making process. BRADO consistently outperforms alternatives in terms of solution quality, achieving fewer threats and better objective function values. To assess BRADO's efficacy, it is benchmarked against several established metaheuristic algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA), Iterated Local Search (ILS), and basic Local Search (LS). The study highlights BRADO's potential as a general-purpose solver for combinatorial problems, opening pathways for future applications in other domains of artificial intelligence.
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